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Automatic Characterization of Stories

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Automatic Characterization of Stories Automatic Characterization of Stories by Sudipta Kar A dissertation submitted to the Department of Computer Science, College of Natural Sciences and Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Chair of Committee: Dr. Thamar Solorio Committee Member: Dr. Carlos Ordonez Committee Member: Dr. Rakesh Verma Committee Member: Dr. Mirella Lapata University of Houston May 2020 Copyright 2020, Sudipta Kar ACKNOWLEDGMENTS April 4, 2015, 3 AM. I just finished watching `The Ph.D. Movie'. I had gotten my student visa a day ago to travel to the USA and start my Ph.D. program. I still remember how scared I became after watching the movie. I was constantly thinking, should I cancel the admission and start preparing for a government job in Bangladesh or take the gamble? Five years later, I am about to finish this milestone and I am happy that I made the right decision that night. From my childhood, I am a huge fan of test cricket matches, where a single game between two teams runs for five consecutive days (each day has three sessions just like the three semesters in an academic year) and at the end, the game can produce no winning team at all. These five years of my Ph.D. journey were exactly like a test match. There were good and bad sessions, tough survival phases, dramas, moments that required strong teamwork and resilience, achievements, and proud moments. I am very happy that I had a lot of people backing me up in this period. First and foremost, I want to thank my amazing advisor Thamar Solorio. She taught me a lot about NLP, Machine Learning, and research during this time. But the most valuable learning from her is that the most important thing to be is a good human being in the first place and have a life outside of work. She was always very supportive, kind, and inspiring. There were numerous periods of extreme frustration. Once I had a one to one meeting with her, everything was fixed within 45 minutes. I could share anything with her and I always knew that while my parents are seven thousand miles away from me, my advisor is here to take care of everything. She is such a wonderful human being. I do not know how many Ph.D. students jammed rock music with their advisors. I am happy to say that I had that chance. Thank you for everything, Professor. I would like to thank Professor Carlos Ordonez and Rakesh Verma for serving my thesis iii committee and providing helpful feedback on my work. I also thank Professor Mirella Lapata for providing tremendous support to my thesis. I had the chance of enjoying her amazing keynote speech at ACL 2017 in Vancouver, Canada. The work in this thesis is very relevant to her research and I wanted to talk to her about it. I was a second-year Ph.D. student and was very hesitant to approach such a giant of NLP. Later I wrote her an email and she surprised me by agreeing to be in my committee. Her thoughtful insights into the research positively transformed my work a lot. I want to thank the past and current members of the RiTUAL group at UH. Everyone in this lab was super helpful, easy to collaborate. Thank you Adri´anPastor L´opez Monroy, Amirreza Shirani, Deepthi Mave, Giovanni Molina, Gustavo Aguilar, John Arevalo, Mahsa Shafaei, Nicolas Rey Villamizar, Niloofar Safi Samghabadi, Prasha Shrestha, Shuguang Chen, Siva Uday Sampreeth Chebolu, Suraj Maharjan, Thoudam Doren Singh, and Yi- geng Zhang. It was nice working with you all and I learned a lot from all of you. I want to give a special thanks to Suraj. He was a big brother in the lab who was always there helping me to learn new things, suggesting good papers, and even teaching deep learning concepts at 1 AM via hangout. I also want to thank Professor Fabio Gonz´alezfor making us familiar with many amazing concepts of Deep Learning when he was visiting us for a semester. NLP conferences were a happy part of this journey. I had the chance to attend many NLP conferences and interact with great minds in the community who encouraged me with a lot of ideas and suggestions. I want to thank Saif Mohammad, Rada Mihalchea, Dan Jurafsky, Ted Pederson, Preslav Nakov. Every time I met them, I came back with a lot of inspiration. I had the great opportunity to serve the NAACL Student Research Workshop' 2019 as a student chair. I want to thank Greg Durret, Na-Rae Han, Farah Nadeem, Laura Burdick who were the co-organizers of the workshop. Organizing this workshop helped me a lot to grow as a researcher. iv During my Ph.D. I had done three wonderful internships at Intel AI, GE Healthcare Data Science team, and Amazon Alexa. These three companies have very different areas of work where AI is being applied and I enjoyed a lot learning diverse things. I want to thank my amazing mentors ChinniKrishna Kothapali (Intel), Harini Eavani (Intel), Hsi-Ming Chang (GE), Ratna Saripalli (GE), Giuseppe Castellucci (Amazon), Simone Filice (Amazon), and Shervin Malmasi (Amazon). Special thanks to Ruhul Amin Shajib, who was my thesis supervisor during my undergrad in SUST, Bangladesh. He was a great mentor throughout my bachelors and even now he did not forget his crazy student and was always there for any discussion. I want to thank Professor M. Zafar Iqbal. These are two great teachers in my life who completely transformed me and for whom today I am doing NLP research. I am grateful to my parents, Prashad Ranjan Kar and Shiuli Bhawal for always being there. I am thankful to my wife Abantika Das Tonny, who was very supportive all the time and dealt with all of my mood swings. I want to thank my younger brother Showmick Kar for being there and indirectly pushing me to be a good example for him. My cousins, relatives, and in-laws were very supportive in this period. Thank you. I want to thank Auklesh Roy, Saimon Hossain, Nasir Uddin, Evanta Kabir, Imran Ahsan, Tahmid Rahman, and Emtiaz Ahmed for making me a part of their lives and families far away from home. Thanks to the people connected with me on social media for cheering to my every success, providing many ideas and encouragement from time to time. While I am writing this, the whole world has been traumatized by the COVID-19 virus. Around three million people are infected by this virus, and 215K people have died. Scientists, doctors, nurses, health workers, and law enforcement officials all over the world are trying their best to help the affected people, find out a vaccine to fight this highly contagious virus, finding out important patterns from data. I want to thank them. They are the heroes. v Dear Parents, I was in the third grade, we were going somewhere on a rickshaw when you two were telling me if I want to write books like what my favorite authors do, I need to study harder. I did not like studying but wanted to write. This little work is dedicated to you. Thank you for giving every bit of what you had for our happiness and education. vi ABSTRACT Computerized systems capable of generating high-level story descriptions have many poten- tial real-life applications. However, enabling computers to do so requires teaching computers to obtain an abstract understanding of natural language stories algorithmically, which is one of the non-trivial problems in Artificial Intelligence and Natural Language Processing. In this dissertation, we tackle the challenge of automatically characterizing stories at a high-level by generating a set of tags from narrative texts written in English. We start by presenting a background study on the problem, discuss the required resources for research, and propose a new corpus to facilitate research on high-level story understanding by selecting tag prediction for movies as an application of this problem. Then, we focus on designing methods for high-level story understanding from written narratives and predicting tags for movies from the written plot synopses. First, we employ a wide range of linguistic features to design a machine learning approach for generating descriptive tags for stories from narrative texts. At the next step, we design a neural methodology for modeling the flow of emotions throughout stories and enhance a system that uses a high-level representation of narrative texts to predict tags. We furthermore exploit the hierarchical structure of text documents to encode the synopses and strengthen the tag prediction mechanism. In the final part of this dissertation, we demonstrate a technique utilizing user reviews to generate tags for characterizing stories at a high-level. We made the new dataset, the source code of the systems, and a live tag prediction system publicly available to the community to encourage further exploration in the direction of automatic story characterization. vii TABLE OF CONTENTS ACKNOWLEDGMENTS . iii ABSTRACT ................................ vii LIST OF TABLES . xiii LIST OF FIGURES . xvi 1 Introduction 1 1.1 Less is more . 2 1.2 Motivation: A Case Study on Movies . 3 1.2.1 The Dilemma of Choice . 3 1.2.2 Letting People Know before They Choose . 6 1.2.3 Applicability . 8 1.3 Research Objectives . 9 1.4 Contributions . 10 1.4.1 Publications . 12 I Fundamentals of High-level Story Characterization 15 2 Background 16 2.1 Computational Narrative Studies: An Overview . 16 2.1.1 Characters and their Activities .
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